OWCM: Optical Wireless Communication Channel Model Library

OWCM is a Pythonic, extensible library for modeling Optical Wireless Communication (OWC) channels across various environments: Atmospheric (FSO), Indoor (VLC/LiFi), Underwater, Underground, and Space.

The library implements a standard taxonomy of channel models (Analytical, Empirical, ML-Enhanced) and creates a unified, composable API for researchers and engineers.

Features

• Multi-Environmental: Dedicated modules for Atmospheric, Indoor, Underwater, Underground, and Space.
• Builder Pattern API: Fluent interface to configure channels with safe defaults.
• Composable Architecture: Channels are built from independent factors (Geometry × Attenuation × Turbulence × Pointing).
• Standard Receipts: Pre-loaded configurations like "mvp", "clear_air", "dense_fog", "los".
• PyPy Friendly: Pure Python implementation with minimal dependencies (numpy, scipy).

Installation

pip install .

Quick Start

Atmospheric Channel (FSO)

Simulate a standard 1km link with Kim attenuation and Gamma-Gamma turbulence.

from owcm import Channel

# 1. Build the channel using the 'mvp' recipe
ch = Channel.environment("atmospheric").defaults("mvp").build(
    distance_m=1000, 
    wavelength_nm=1550, 
    pt_w=0.5,            # Transmit power (Watts)
    visibility_km=2.0,   # Visibility for attenuation
    # Additional parameters override defaults
    theta_rad=1e-3,      # Beam divergence
    gg_alpha=3.2,        # Gamma-Gamma alpha
    gg_beta=2.1          # Gamma-Gamma beta
)

# 2. Calculate average Received Power
pr = ch.received_power()
print(f"Received Power: {pr:.3e} W")

# 3. Calculate Outage Probability (P(Pr < threshold))
outage = ch.outage(threshold_w=1e-6, n=100000)
print(f"Outage Probability: {outage:.4f}")

Indoor Channel (VLC)

Simulate a Line-of-Sight (LoS) Lambertian link.

ch_in = Channel.environment("indoor").defaults("los").build(
    distance_m=3.0, 
    lambertian_order=1.0, 
    DR_m=0.01
)
print(f"Indoor Only Gain: {ch_in.gain():.3e}")

Underwater Channel

Beer-Lambert extinction model.

ch_uw = Channel.environment("underwater").defaults("beer_lambert").build(
    distance_m=10.0, 
    attenuation_c=0.1 # Extinction coefficient (1/m)
)

Architecture

The library is structured as follows:

OWCM/
  owc/
    core/           # Types, Registry, Composite Logic
    atmospheric/    # FSO Models
    indoor/         # VLC Models
    underwater/     # Underwater Models
    underground/    # Underground Models
    space/          # Space Models

Each environment has a recipes.py defining default model stacks (e.g., mvp, clear_air).

License

MIT